Journal of Food Measurement and Characterization

, Volume 11, Issue 4, pp 2142–2150 | Cite as

Computer vision coupled with laser backscattering for non-destructive colour evaluation of papaya during drying

  • Patchimaporn UdomkunEmail author
  • Marcus Nagle
  • Dimitrios Argyropoulos
  • Alexander Nimo Wiredu
  • Busarakorn Mahayothee
  • Joachim Müller
Original Paper


Colour change is a common physical phenomenon observed during drying, which need to be controlled since it directly affects consumer acceptance of dried products. This study aimed to investigate the feasibility of using computer vision, combined with laser light backscattering analysis at 650 nm in order to predict colour changes of papaya during drying. The results revealed that each image-processing factor obtained can potentially be used to describe every colour attribute change, except for chroma value. The multivariate correlations of measured backscattering parameters as well as the digital image properties were found to yield the best fitting for colour validations. Interestingly, the use of computer vision technique coupled with laser backscattering methods provides a reliable tool for quality control based on a rapid, consistent, and non-intrusive method for in-line quality measurement in established fruit drying processes.


Colour change Laser backscattering Computer vision system Quality control Drying Papaya 

List of symbols


Initial value


Total colour difference


Intensity in green-red


Illuminated area (pixel)


Intensity in blue-yellow




Colour attribute


Colour image of papaya


Experimentally observed colour attribute


Predicted colour attribute


Chroma meter


Computer vision systems


Converted value sR, sG, or sB


Hue (°)


Light intensity




Mean absolute percentage error (%)


Number of observations




Red, green, and blue


Relative humidity (%)


Coefficients of determination

sR, sG, sB

Components of sRGB




Value of R′, G′, or B′


Image attribute

Xn, Yn, Zn

XYZ values of a reference white colour



This research was the result of a scholarship from the Food Security Center of Universität Hohenheim, which is part of the DAAD (German Academic Exchange Service) Program “Exceed”. It was also supported by DAAD and the German Federal Ministry for Economic Cooperation and Development (BMZ). This research was also undertaken as part of the CGIAR Research Program on Agriculture for Nutrition and Health, and supported by International Institute of Tropical Agriculture (IITA). The authors gratefully acknowledge the International Fund for Agricultural Development (IFAD) and the European Commission (EC) (PJ-002057) for giving the opportunity to prepare this article. The authors also acknowledge the contributions of the “Feed the future Mozambique improved seeds for better agriculture (SEMEAR)” which supported co-authorships of the paper. We gratefully acknowledge the financial support. We are also grateful to Mrs. Ingrid Amberg and Mrs. Dorothea Hirschbach-Müller for their technical support.


  1. 1.
    I. Alibas, B. Akbudak, N. Akbudak, Microwave drying characteristics of spinach. J. Food Eng. 78(2), 577–583 (2005)Google Scholar
  2. 2.
    D. Argyropoulos, A. Heindl, J. Müller, Assessment of convection, hot-air combined with microwave-vacuum and freeze-drying methods for mushroom with regard to product quality. Int. J. Food Sci. Technol. 46(2), 333–342 (2011)CrossRefGoogle Scholar
  3. 3.
    G.S. Birth, How light interacts with foods. in Quality Detection in Foods, ed. by J.J. Gaffney Jr. (American Society for Agricultural Engineering, St. Joseph, 1976), pp. 6–11Google Scholar
  4. 4.
    V. Briones, J.M. Aguilera, Image analysis of changes in surface color of chocolate. Food Res. Int. 38, 87–94 (2005)CrossRefGoogle Scholar
  5. 5.
    T. Brosnan, D.W. Sun, Improving quality inspection of food products by computer vision—a review. J. Food Eng. 61(1), 3–16 (2003)CrossRefGoogle Scholar
  6. 6.
    C. Costa, F. Antonucci, F. Pallottino, J. Aguzzi, D.W. Sun, P. Menesatti, Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food Bioprocess Technol. 4(5), 673–692 (2011)CrossRefGoogle Scholar
  7. 7.
    E.R. Davies, The application of machine vision to food and agriculture: a review. Imaging Sci. J. 57(4), 197–217 (2009)CrossRefGoogle Scholar
  8. 8.
    I. Karabulut, A. Topcu, A. Duran, S. Turan, B. Ozturk, Effect of hot air drying and sun drying on color values and β-carotene content of apricot (Prunus armenica L.). LWT-Food Sci. Technol. 40(5), 753–758 (2007)CrossRefGoogle Scholar
  9. 9.
    M.K. Krokida, E. Tsami, Z.B. Maroulis, Kinetics on color changes during drying of some fruits and vegetables. Drying Technol. 16(3–5), 667–685 (1998)CrossRefGoogle Scholar
  10. 10.
    K. León, D. Mery, F. Pedreschi, J. León, Color measurement in L*a*b* units from RGB digital images. Food Res. Int. 39, 1084–1091 (2006)CrossRefGoogle Scholar
  11. 11.
    M. Maskan, Drying, shrinkage and rehydration characteristics of kiwifruits during hot air and microwave drying. J. Food Eng. 48, 177–182 (2001)CrossRefGoogle Scholar
  12. 12.
    F. Mendoza, J.M. Aguilera, Application of image analysis for classification of ripening bananas. J. Food Sci. 69, E471–E477 (2004)CrossRefGoogle Scholar
  13. 13.
    F. Mendoza, P. Dejmek, J.M. Aguilera, Calibrated colour measurements of agricultural foods using image analysis. Postharvest Biol. Technol. 41(3), 285–295 (2006)CrossRefGoogle Scholar
  14. 14.
    K. Mollazade, M. Omid, F.A. Tab, S.S. Mohtasebi, Principles and applications of light backscattering imaging in quality evaluation of agro-food products: a review. Food Bioprocess Technol. 5, 1465–1485 (2012)CrossRefGoogle Scholar
  15. 15.
    M. Nagle, K. Intani, G. Romano, B. Mahayothee, V. Sardsud, J. Müller, Determination of surface color of ‘all yellow’ mango cultivars using computer vision. Int. J. Agric. Biol. Eng. 9(1), 42–50 (2016)Google Scholar
  16. 16.
    C.O. Perera, Selected quality attributes of dried foods. Drying Technol. 23(4), 717–730 (2005)CrossRefGoogle Scholar
  17. 17.
    I. Pott, M. Marx, S. Neidhart, W. Mühlbauer, R. Carle, Quantitative determination of β-carotene stereoisomers in fresh, dried, and solar-dried mangoes (Mangifera indica L.). J. Agric. Food Chem. 51(16), 4527–4531 (2003)CrossRefGoogle Scholar
  18. 18.
    Rec. ITU-R BT.709-5, Parameters Values for the HDTV Standards for Production and International Programme Exchange (1990, revised 2002) (International Telecommunication Union, Geneva, Switzerland, 2002)Google Scholar
  19. 19.
    G. Romano, M. Nagle, D. Argyropoulos, J. Müller, Laser light backscattering to monitor moisture content, soluble solid content and hardness of apple tissue during drying. J. Food Eng. 104, 657–662 (2011)CrossRefGoogle Scholar
  20. 20.
    G. Romano, D. Argyropoulos, M. Nagle, M.T. Khan, J. Müller, Combination of digital images and laser light to predict moisture content and color of bell pepper simultaneously during drying. J. Food Eng. 109, 438–448 (2012)CrossRefGoogle Scholar
  21. 21.
    P. Udomkun, M. Mahayothee, M. Nagle, J. Müller, Effects of calcium chloride and calcium lactate applications with osmotic pretreatment on physicochemical aspects and consumer acceptances of dried papaya. Int. J. Food Sci. Technol. 49, 1122–1131 (2014)CrossRefGoogle Scholar
  22. 22.
    P. Udomkun, M. Nagle, B. Mahayothee, J. Müller, Laser-based imaging system for non-invasive monitoring of quality changes of papaya during drying. J. Food Control 42, 225–233 (2014)CrossRefGoogle Scholar
  23. 23.
    P. Udomkun, M. Nagle, B. Mahayothee, D. Nohr, A. Koza, J. Müller, Influence of air drying properties on non-enzymatic browning, major bio-active compounds and antioxidant capacity of osmotically pretreated papaya. LWT-Food Sci. Technol. 60(2), 914–922 (2015)CrossRefGoogle Scholar
  24. 24.
    P. Udomkun, M. Nagle, D. Argyropoulos, J. Müller, Multi-sensor approach to improve optical monitoring of papaya shrinkage during drying. J. Food Eng. 189, 82–89 (2016)Google Scholar
  25. 25.
    A. Vega-Gálvez, K. Di Scala, K. Rodríguez, R. Lemus-Mondaca, M. Miranda, J. López, M. Perez-Won, Effect of air-drying temperature on physico-chemical properties, antioxidant capacity, colour and total phenolic content of red pepper (Capsicum annuum, L. var. Hungarian). Food. Chem. 117(4), 647–653 (2009)CrossRefGoogle Scholar
  26. 26.
    D. Wu, D.W. Sun, Colour measurements by computer vision for food quality control—A Review. Trends Food Sci. Technol. 29, 5–20 (2013)CrossRefGoogle Scholar
  27. 27.
    K.L. Yam, S.E. Papadakis, A simple digital imaging method for measuring and analyzing color of food surfaces. J. Food Eng. 61, 137–142 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Patchimaporn Udomkun
    • 1
    Email author
  • Marcus Nagle
    • 2
  • Dimitrios Argyropoulos
    • 2
  • Alexander Nimo Wiredu
    • 3
  • Busarakorn Mahayothee
    • 4
  • Joachim Müller
    • 2
  1. 1.International Institute of Tropical Agriculture (IITA)BukavuDemocratic Republic of the Congo
  2. 2.Institute of Agricultural Engineering, Tropics and Subtropics GroupUniversität HohenheimStuttgartGermany
  3. 3.International Institute of Tropical Agriculture (IITA)NampulaMozambique
  4. 4.Department of Food TechnologySilpakorn UniversityNakhon PathomThailand

Personalised recommendations